Book Image

Deep Learning for Genomics

By : Upendra Kumar Devisetty
Book Image

Deep Learning for Genomics

By: Upendra Kumar Devisetty

Overview of this book

Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you’ll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you’ll have learned about the challenges, best practices, and pitfalls of deep learning for genomics.
Table of Contents (18 chapters)
Part 1 – Machine Learning in Genomics
Part 2 – Deep Learning for Genomic Applications
Part 3 – Operationalizing models


In this first chapter, you were introduced to the concept of ML for genomics. We gained a brief understanding of ML in several genomic applications in the life science, pharma, clinical, and biotechnology industries. We also looked at the rapid strides that NGS has made in the last 15 years and how it contributed to the production of genomic big data. Then, we understood how ML can be used to analyze genomic data for the development of genomic-based products.

Finally, we looked at the different programming languages, including the most popular genomic library and ML software that we will be using throughout this book. You will mainly use Python and scikit-learn for developing models, Biopython for genomic data analysis, and some open source tools for model training and productionalizing them for deploying models.

In the next chapter, we will introduce the fundamentals of genomic data analysis.